This application represents an exciting GWAS (Genome-Wide Association Study) to identify genes associated with edentulism, gingivitis and periodontitis. We intend to perform a GWAS meta-analysis using two primary databases; one with 6,786 subjects [from the Dental- Atherosclerosis Risk in Communities (DARIC) Study] and another with 4,308 subjects [from the Study of Health in Pomerania (SHIP) Study]. Both datasets have whole genomic genotyping data recently created using the Affymetrix Human SNP Array 6.0 and have detailed clinical periodontal examination data, and complete medical and risk factor data. We intend to use a meta-analysis approach combining these two datasets to identify genes that confer risk for edentulism, gingivitis and periodontitis. A third dataset of 3075 subjects [Health and Body Composition (HealthABC) Study] with periodontal phenotypes and genotype data created using the Illumina 1m SNP chip platform will be used for replication of these findings. This represents a collaboration that brings together the dental researchers at the UNC School of Dentistry using the Dental ARIC data, and the genetic epidemiologists at the UNC School of Public Health (DARIC) and two other groups - U of Greifswald Germany (SHIP) and UCLA/U of Pittsburgh (HealthABC). Together, we propose to perform what to our knowledge will be the first genome-wide survey for genes which are associated with periodontal disease in a representative adult population. We are fortunate to work with an outstanding genetic epidemiologist, Dr Kari North who will lead the genetic survey and statistical analyses. We have an approved ARIC protocol to analyze these data for gene associations for periodontal disease and have a commitment for the sharing of the SHIP and HealthABC datasets for the met-analysis and replication. We intend to conduct a GWAS applying various clinical case definitions of periodontal disease using existing data from both D-ARIC (n=6786) and SHIP (n=4,308). We propose to develop race-specific models for gene-wide associations using various definitions of oral disease - either categorical classifications of disease using clinically relevant clusters of clinical signs to define case status or by defining clinical phenotypes using clinical signs independently as continuous variables, such as mean interproximal attachment loss. We propose to analyze the two datasets independently and then conduct a meta-analysis pooling them, using harmonized variable definitions for the phenotypes. We also intend to examine for gene-environment interactions focusing on smoking, obesity, diabetes and microbial burden as effect modifiers. Finally, we will perform a replication analysis using the HealthABC dataset. Our goal is to identifying novel genes that confer either susceptibility or resistance to periodontal disease to enable us to usher in a new generation of periodontal diagnostics, risk assessments and enable targeted therapeutics; as a realization of personalized medicine.